6 citations · 13 across the 4 of their papers we have counts for
4 papers
Decision-making with Speculative Opponent Models
Jing Sun, Shuo Chen, Cong Zhang +2
Opponent modelling has proven effective in enhancing the decision-making of the controlled agent by constructing models of opponent agents. However, existing methods often rely on…
Deep Reinforcement Learning Guided Improvement Heuristic for Job Shop Scheduling
Cong Zhang, Zhiguang Cao, Wen Song +2
Recent studies in using deep reinforcement learning (DRL) to solve Job-shop scheduling problems (JSSP) focus on construction heuristics. However, their performance is still far fro…
Learning to Solve Multiple-TSP with Time Window and Rejections via Deep Reinforcement Learning
Rongkai Zhang, Cong Zhang, Zhiguang Cao +5
We propose a manager-worker framework based on deep reinforcement learning to tackle a hard yet nontrivial variant of Travelling Salesman Problem (TSP), \ie~multiple-vehicle TSP wi…
Sampling Efficient Deep Reinforcement Learning through Preference-Guided Stochastic Exploration
Wenhui Huang, Cong Zhang, Jingda Wu +3
Massive practical works addressed by Deep Q-network (DQN) algorithm have indicated that stochastic policy, despite its simplicity, is the most frequently used exploration approach.…